Datasets:
File size: 7,030 Bytes
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import argparse
import glob
import os
import dedalus.public as d3
import h5py as h5
import numpy as np
def populate_empty_file(file):
create_dimensions(file)
create_base_attributes(file)
create_field_types(file)
def create_boundary_conditions(file):
bcs = file.create_group("boundary_conditions")
x = bcs.create_group("x_periodic")
x.attrs["associated_dims"] = ["phi"]
x.attrs["bc_type"] = "PERIODIC"
x.attrs["associated_fields"] = []
x.attrs["sample_varying"] = False
x.attrs["time_varying"] = False
mask = np.zeros_like(file["dimensions"]["phi"], dtype=bool)
mask[0] = True
mask[-1] = True
xds = x.create_dataset("mask", data=mask, dtype=bool)
y = bcs.create_group("y_open")
y.attrs["associated_dims"] = ["theta"]
y.attrs["bc_type"] = "OPEN"
y.attrs["associated_fields"] = []
mask = np.zeros_like(file["dimensions"]["theta"], dtype=bool)
mask[0] = True
mask[-1] = True
yds = y.create_dataset("mask", data=mask, dtype=bool)
y.attrs["sample_varying"] = False
y.attrs["time_varying"] = False
def create_base_attributes(file):
file.attrs["dataset_name"] = "dataset"
file.attrs["n_spatial_dims"] = 3
file.attrs["simulation_parameters"] = []
file.attrs["grid_type"] = "cartesian"
def create_field_types(file):
field_types = ["t0_fields", "t1_fields", "t2_fields", "scalars"]
for field_type in field_types:
gr = file.create_group(field_type)
gr.attrs["field_names"] = []
def create_dimensions(file):
file.create_group("dimensions")
file["dimensions"].attrs["spatial_dims"] = ["phi", "theta"]
# file['dimensions'].create_dataset('time', data=np.array([0]))
def earthswe_to_well(in_path, out_path):
print("Starting file copy!")
orig_file = h5.File(in_path, "r")
print("orig keys", list(orig_file.keys()))
if os.path.exists(out_path):
os.remove(out_path)
with h5.File(out_path, "w") as new_file:
populate_empty_file(new_file)
## First populate the attributes
new_file.attrs["dataset_name"] = "planetswe"
new_file.attrs["n_spatial_dims"] = 2
new_file.attrs["simulation_parameters"] = []
new_file.attrs["grid_type"] = "equiangular"
print("orig_file", orig_file.keys())
new_file.attrs["n_trajectories"] = 1 # orig_file['c'].shape[0]
# Make attributes for each simulation parameter
# parameter_string = in_path.split('/')[-1][:-5].split('_')
# print(parameter_string)
new_file["scalars"].attrs["field_names"] = new_file.attrs[
"simulation_parameters"
]
# Now let's populate the dimensions
new_file["dimensions"].attrs["spatial_dims"] = ["theta", "phi"]
time = new_file["dimensions"].create_dataset(
"time", data=orig_file["scales"]["sim_time"], dtype="f4"
)
time.attrs["sample_varying"] = False
# Same coordinates for x and y in this specific data
print(orig_file["scales"].keys())
d = new_file["dimensions"].create_dataset(
"phi",
data=orig_file["scales"][
"phi_hash_7b8ec7cabc40ac4b596a5ef833e9eab019f07d46"
],
dtype="f4",
)
d.attrs["time_varying"] = False
d.attrs["sample_varying"] = False
d = new_file["dimensions"].create_dataset(
"theta",
data=orig_file["scales"][
"theta_hash_47f1a1c5acad69381fef2149e23fb804716211f6"
],
dtype="f4",
)
d.attrs["time_varying"] = False
d.attrs["sample_varying"] = False
h, u = dedalus_interpolate(
orig_file["tasks"]["h"][:], orig_file["tasks"]["u"][:]
)
# T0 Data
new_file["t0_fields"].attrs["field_names"] = ["height"]
f = new_file["t0_fields"].create_dataset(
"height", data=np.transpose(h[np.newaxis, ...], (0, 1, 3, 2)), dtype="f4"
)
f.attrs["time_varying"] = True
f.attrs["sample_varying"] = True
f.attrs["dim_varying"] = [True, True]
# T1 Data
new_file["t1_fields"].attrs["field_names"] = ["velocity"]
f = new_file["t1_fields"].create_dataset(
"velocity",
data=np.transpose(u[np.newaxis, ...], (0, 1, 4, 3, 2)),
dtype="f4",
)
f.attrs["time_varying"] = True
f.attrs["sample_varying"] = True
f.attrs["dim_varying"] = [True, True]
# T2 Data
new_file["t2_fields"].attrs["field_names"] = []
create_boundary_conditions(new_file)
def dedalus_interpolate(h, u):
meter = 1 / 6.37122e6
hour = 1
second = hour / 3600
g = 9.80616 * meter / second**2
Nphi = 512
Ntheta = 256
dtype = np.float64
coords = d3.S2Coordinates("phi", "theta")
dist = d3.Distributor(coords, dtype=dtype)
basis = d3.SphereBasis(coords, (Nphi, Ntheta), radius=1, dealias=1, dtype=dtype)
h3 = dist.Field(name="h", bases=basis)
u3 = dist.VectorField(coords, name="u", bases=basis)
nphi = h.shape[1]
ntheta = h.shape[2]
u_out = np.zeros(u.shape)
h_out = np.zeros(h.shape)
delta = np.pi / (ntheta + 1)
for j in range(u.shape[0]):
if j % 50 == 0:
print("row", j)
u3["g"] = u[j]
h3["g"] = h[j]
print("field shape!", u3["g"].shape)
for i, pt in enumerate(np.linspace(np.pi - delta / 2, delta / 2, ntheta)):
u_interp = d3.Interpolate(u3, "theta", pt).evaluate()["g"]
h_interp = d3.Interpolate(h3, "theta", pt).evaluate()["g"]
u_out[j, ..., i : i + 1] = u_interp * second / meter
h_out[j, ..., i : i + 1] = h_interp / meter
return h_out, u_out
if __name__ == "__main__":
print("HAVE WE EVEN STARTED CODE YET?")
parser = argparse.ArgumentParser()
parser.add_argument(
"--source",
default="/mnt/home/polymathic/ceph/the_well/testing_before_adding/earthswe",
)
parser.add_argument(
"--dest", default="/mnt/home/polymathic/ceph/the_well/datasets/planetswe/data"
)
parser.add_argument("--index", default="0")
args = parser.parse_args()
current_path = args.source
write_path = args.dest
ic_file = int(args.index)
max_ic_train = 32
max_ic_valid = 36
ic_folders = sorted(glob.glob(f"{current_path}/IC*"))
target_ic = ic_folders[ic_file]
print("picked source", target_ic)
# for i, folder in enumerate(ic_folders):
ic_num = int(target_ic.split("_")[-1])
if ic_num < max_ic_train:
split = "train"
elif ic_num < max_ic_valid:
split = "valid"
else:
split = "test"
for i in range(10):
print(i)
for file in glob.glob(f"{target_ic}/*.h5"):
file_idx = file.split("_")[-1][:-3]
target_path = f"{write_path}/{split}/planetswe_IC{ic_num:02d}_{file_idx}.h5"
print(file, target_path)
earthswe_to_well(file, target_path)
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